update default model to llama3.2 (#6959)

examples/python-grounded-factuality-rag-check/README.md

 # RAG Hallucination Checker using Bespoke-Minicheck
 
-This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retreived from the document and given to `llama3.1` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations. 
+This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retreived from the document and given to `llama3.2` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations. 
 
 ## Running the Example
 
-1. Ensure `all-minilm` (embedding) `llama3.1` (chat) and `bespoke-minicheck` (check) models installed:
+1. Ensure `all-minilm` (embedding) `llama3.2` (chat) and `bespoke-minicheck` (check) models installed:
 
    ```bash
    ollama pull all-minilm
-   ollama pull llama3.1
+   ollama pull llama3.2
    ollama pull bespoke-minicheck
    ```
 

examples/python-grounded-factuality-rag-check/main.py

@@ -119,7 +119,7 @@ if __name__ == "__main__":
         system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
 
         ollama_response = ollama.generate(
-            model="llama3.1",
+            model="llama3.2",
             prompt=question,
             system=system_prompt,
             options={"stream": False},

examples/python-json-datagenerator/predefinedschema.py

@@ -2,7 +2,7 @@ import requests
 import json
 import random
 
-model = "llama3.1"
+model = "llama3.2"
 template = {
   "firstName": "",
   "lastName": "",

examples/python-json-datagenerator/randomaddresses.py

@@ -12,7 +12,7 @@ countries = [
     "France",
 ]
 country = random.choice(countries)
-model = "llama3.1"
+model = "llama3.2"
 
 prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."
 

examples/python-json-datagenerator/readme.md

@@ -6,10 +6,10 @@ There are two python scripts in this example. `randomaddresses.py` generates ran
 
 ## Running the Example
 
-1. Ensure you have the `llama3.1` model installed:
+1. Ensure you have the `llama3.2` model installed:
 
    ```bash
-   ollama pull llama3.1
+   ollama pull llama3.2
    ```
 
 2. Install the Python Requirements.

examples/python-simplechat/client.py

@@ -2,7 +2,7 @@ import json
 import requests
 
 # NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
-model = "llama3.1"  # TODO: update this for whatever model you wish to use
+model = "llama3.2"  # TODO: update this for whatever model you wish to use
 
 
 def chat(messages):

examples/python-simplechat/readme.md

@@ -4,10 +4,10 @@ The **chat** endpoint is one of two ways to generate text from an LLM with Ollam
 
 ## Running the Example
 
-1. Ensure you have the `llama3.1` model installed:
+1. Ensure you have the `llama3.2` model installed:
 
    ```bash
-   ollama pull llama3.1
+   ollama pull llama3.2
    ```
 
 2. Install the Python Requirements.

examples/typescript-simplechat/client.ts

 import * as readline from "readline";
 
-const model = "llama3.1";
+const model = "llama3.2";
 type Message = {
   role: "assistant" | "user" | "system";
   content: string;

macapp/src/app.tsx

@@ -19,7 +19,7 @@ export default function () {
   const [step, setStep] = useState<Step>(Step.WELCOME)
   const [commandCopied, setCommandCopied] = useState<boolean>(false)
 
-  const command = 'ollama run llama3.1'
+  const command = 'ollama run llama3.2'
 
   return (
     <div className='drag'>